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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
cc70d3f907
* feat(Selection): added prototype feature selection python script * feat(Utils): added some helpers for the future from Advances in Financial ML book * feat(Selection): added RFECV * feat(Selection): added configurable feature selection step into pipeline * feat(Config): added level_1 & level_2 default config, PCA before feature selection process starts * feat(Selection): added backup feature selector models if current one can't output feature importance, removed unnecessary array for level-2 models * fix(Training): deal with zero first value coming out of static models * feat(Sweep): added feature selection sweep * fix(Sweep): config problem * fix(Sweep): config * chore(Utils): removed unnecessary purged k-fold crossval class * feat(Config): added dimensionality_reduction as a separate flag * fix(Sweep): config updated * fix(Sweep): sweep name * chore(Config): updated level_2 config to the best performing configuation
24 lines
1.1 KiB
Python
24 lines
1.1 KiB
Python
from sklearn.feature_selection import RFE
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from sklearn.model_selection import TimeSeriesSplit
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import pandas as pd
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from models.base import Model, SKLearnModel
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from sklearn.decomposition import PCA
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def select_features(X: pd.DataFrame, y: pd.Series, model: Model, min_features_to_select: int, backup_model: SKLearnModel) -> pd.DataFrame:
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''' Select features using RFECV, returns a pd.DataFrame (X) with only the selected features.'''
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if model.model_type != 'ml': return X
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# 2. Recursive feature selection
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cv = TimeSeriesSplit(n_splits=5)
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feat_selector_model = model.model
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if hasattr(feat_selector_model, 'feature_importances_') == False and hasattr(feat_selector_model, 'coef_') == False:
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feat_selector_model = backup_model.model
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# selector = RFECV(feat_selector_model, cv = cv, step=5, min_features_to_select=min_features_to_select)
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selector = RFE(feat_selector_model, n_features_to_select=10)
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selector = selector.fit(X, y)
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print("Kept %d features out of %d" % (selector.n_features_, X.shape[1]))
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return pd.DataFrame(X[X.columns[selector.support_]], index= X.index)
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